nf-core/airrflow: An adaptive immune receptor repertoire analysis workflow employing the Immcantation framework

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor se...

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Published inPLoS computational biology Vol. 20; no. 7; p. e1012265
Main Authors Gabernet, Gisela, Marquez, Susanna, Bjornson, Robert, Peltzer, Alexander, Meng, Hailong, Aron, Edel, Lee, Noah Y., Jensen, Cole G., Ladd, David, Polster, Mark, Hanssen, Friederike, Heumos, Simon, Yaari, Gur, Kowarik, Markus C., Nahnsen, Sven, Kleinstein, Steven H.
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 26.07.2024
Public Library of Science (PLoS)
Subjects
Adaptive Immunity - genetics
B-Lymphocytes - immunology
Biology and Life Sciences
Cell receptors
Computational Biology - methods
COVID-19 - genetics
COVID-19 - immunology
COVID-19 - virology
Engineering and Technology
High-Throughput Nucleotide Sequencing - methods
High-throughput screening (Biochemical assaying)
Humans
Immune response
Medicine and Health Sciences
Methods
Physiological aspects
Receptors, Antigen, B-Cell - genetics
Receptors, Antigen, B-Cell - immunology
Receptors, Antigen, T-Cell - genetics
Receptors, Antigen, T-Cell - immunology
Research and Analysis Methods
SARS-CoV-2 - genetics
SARS-CoV-2 - immunology
Single-Cell Analysis - methods
Software
T-Lymphocytes - immunology
Workflow
United States
Online AccessGet full text
ISSN1553-7358
1553-734X
1553-7358
DOI10.1371/journal.pcbi.1012265

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Abstract Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets.
AbstractList Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets.
Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets. We have created nf-core/airrflow, a workflow to help researchers study the immune system in healthy and disease states, such as infections, autoimmunity, and cancer. The adaptive immune system is responsible for the third line of defense responses, specific to each particular threat, after physical barriers have been compromised and the nonspecific innate immune response has failed to clear the danger. Two types of white blood cells are central players in the adaptive response, namely B cells and T cells. These cells have surface receptors that recognize suspicious elements (antigens). Learning what receptors bind to which antigens is of utmost interest to understand immune responses. Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a technique that allows to determine the genetic sequence of these receptors. The amount of data generated in these experiments is large, and the analysis complex. nf-core/airrflow simplifies running a comprehensive analysis connecting tools from Immcantation, a specialized software project to analyze AIRR-seq data. The workflow can efficiently process large datasets on multiple computing platforms. We analyzed immune responses to COVID-19 in 97 infected individuals and 99 healthy people, and confirmed previous findings and provided new insights, demonstrating the workflow’s applicability to reanalyzing large publicly available datasets.
Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets.Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets.
Audience Academic
Author Jensen, Cole G.
Aron, Edel
Kleinstein, Steven H.
Gabernet, Gisela
Ladd, David
Polster, Mark
Hanssen, Friederike
Heumos, Simon
Yaari, Gur
Nahnsen, Sven
Marquez, Susanna
Kowarik, Markus C.
Bjornson, Robert
Peltzer, Alexander
Meng, Hailong
Lee, Noah Y.
AuthorAffiliation 3 Yale Center for Research Computing, New Haven, Connecticut, United States of America
4 Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
8 M3 Research Center, University Hospital, Tübingen, Germany
2 Quantitative Biology Center, Eberhard-Karls University of Tübingen, Tübingen, Germany
12 Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard-Karls University of Tübingen, Tübingen, Germany
13 Department of Immunobiology, Yale School of Medicine, New Haven, Connecticut, United States of America
11 Hertie Institute for Clinical Brain Research, Eberhard-Karls University of Tübingen, Tübingen, Germany
6 oNKo-Innate Pty Ltd, Melbourne, Victoria, Australia
9 Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
University of Toulouse III Paul Sabatier, Center of Integrative Biology & INRIA Saclay, FRANCE
7 Department of Computer Science, Eberhard-Karls University of Tübingen, Tübingen, Germany
1 Department of Pathology, Yale School of Medicine, New Haven, Connecticut
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I have read the journal’s policy and the authors of this manuscript have the following competing interests: SHK receives consulting fees from Peraton. AP is an employee of Boehringer Ingelheim Pharma GmbH & Co KG and declares no conflict of interest. DL is an employee of oNKo-innate Pty Ltd and declares no conflict of interest. MCK has served on advisory boards and received speaker fees / travel grants from Merck, Sanofi-Genzyme, Novartis, Biogen, Janssen, Alexion, Celgene / Bristol-Myers Squibb and Roche. He has received research grants from Merck, Roche, Novartis, Sanofi-Genzyme and Celgene / Bristol-Myers Squibb. All other authors declare no conflicts of interest.
Membership of ‘nf-core community’ is provided in the Acknowledgements.
These authors are joint senior authors on this work.
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PublicationDate 20240726
PublicationDateYYYYMMDD 2024-07-26
PublicationDate_xml – month: 7
  year: 2024
  text: 20240726
  day: 26
PublicationDecade 2020
PublicationPlace United States
PublicationPlace_xml – name: United States
– name: San Francisco, CA USA
PublicationTitle PLoS computational biology
PublicationTitleAlternate PLoS Comput Biol
PublicationYear 2024
Publisher Public Library of Science
Public Library of Science (PLoS)
Publisher_xml – name: Public Library of Science
– name: Public Library of Science (PLoS)
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Snippet Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges...
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SubjectTerms Adaptive Immunity - genetics
B-Lymphocytes - immunology
Biology and Life Sciences
Cell receptors
Computational Biology - methods
COVID-19 - genetics
COVID-19 - immunology
COVID-19 - virology
Engineering and Technology
High-Throughput Nucleotide Sequencing - methods
High-throughput screening (Biochemical assaying)
Humans
Immune response
Medicine and Health Sciences
Methods
Physiological aspects
Receptors, Antigen, B-Cell - genetics
Receptors, Antigen, B-Cell - immunology
Receptors, Antigen, T-Cell - genetics
Receptors, Antigen, T-Cell - immunology
Research and Analysis Methods
SARS-CoV-2 - genetics
SARS-CoV-2 - immunology
Single-Cell Analysis - methods
Software
T-Lymphocytes - immunology
Workflow
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Title nf-core/airrflow: An adaptive immune receptor repertoire analysis workflow employing the Immcantation framework
URI https://www.ncbi.nlm.nih.gov/pubmed/39058741
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Volume 20
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